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README.md

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 This model was converted to GGUF format from [`utter-project/EuroLLM-9B-Instruct`](https://huggingface.co/utter-project/EuroLLM-9B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/utter-project/EuroLLM-9B-Instruct) for more details on the model.
 
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-Model details:
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-This is the model card for EuroLLM-9B-Instruct. You can also check the pre-trained version: EuroLLM-9B.
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-Developed by: Unbabel, Instituto Superior Técnico, 
-Instituto de Telecomunicações, University of Edinburgh, Aveni, 
-University of Paris-Saclay, University of Amsterdam, Naver Labs, 
-Sorbonne Université.
-Funded by: European Union.
-Model type: A 9B parameter multilingual transfomer LLM.
-Language(s) (NLP): Bulgarian, Croatian, Czech, 
-Danish, Dutch, English, Estonian, Finnish, French, German, Greek, 
-Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, 
-Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Arabic, 
-Catalan, Chinese, Galician, Hindi, Japanese, Korean, Norwegian, Russian,
- Turkish, and Ukrainian. 
-License: Apache License 2.0.
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-		Model Details
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-The EuroLLM project has the goal of creating a suite of LLMs capable 
-of understanding and generating text in all European Union languages as 
-well as some additional relevant languages.
-EuroLLM-9B is a 9B parameter model trained on 4 trillion tokens divided 
-across the considered languages and several data sources: Web data, 
-parallel data (en-xx and xx-en), and high-quality datasets.
-EuroLLM-9B-Instruct was further instruction tuned on EuroBlocks, an 
-instruction tuning dataset with focus on general instruction-following 
-and machine translation.
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-		Model Description
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-EuroLLM uses a standard, dense Transformer architecture:
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-We use grouped query attention (GQA) with 8 key-value heads, since 
-it has been shown to increase speed at inference time while maintaining 
-downstream performance.
-We perform pre-layer normalization, since it improves the training stability, and use the RMSNorm, which is faster.
-We use the SwiGLU activation function, since it has been shown to lead to good results on downstream tasks.
-We use rotary positional embeddings (RoPE) in every layer, since 
-these have been shown to lead to good performances while allowing the 
-extension of the context length.
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-For pre-training, we use 400 Nvidia H100 GPUs of the Marenostrum 5 
-supercomputer, training the model with a constant batch size of 2,800 
-sequences, which corresponds to approximately 12 million tokens, using 
-the Adam optimizer, and BF16 precision.
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 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)